Monthly rainfall hind-cast using machine learning algorithms for Coimbatore, Tamil Nadu

Authors

  • V. GEETHALAKSHMI Crop Management, Tamil Nadu Agricultural University, Coimbatore – 641 003, India
  • S. KOKILAVANI Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore – 641 003, India
  • S.P. RAMANATHAN Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore – 641 003, India
  • GA. DHEEBAKARAN Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore – 641 003, India
  • N.K. SATHYAMOORTHY Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore – 641 003, India
  • N. MARAGATHAM Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore – 641 003, India

DOI:

https://doi.org/10.54302/mausam.v73i1.5077

Keywords:

Rainfall, K-means, Decision tree regression, Gradient boosting, Ada boost, Hindcast, Random forest regression

Abstract

 

Due to current world climate change, the accuracy of predicting rainfall is critical. This paper presents an approach using four different machine learning algorithms, viz., Decision Tree Regression (DTR), Gradient Boosting (GB), Ada Boost (AB) and Random Forest Regression (RFR) techniques to improve the rainfall forecast performance. When historical events are entered into the model and get validated to realise how well the output suits the known results referred as Hind-cast. Historical monthly weather parameters over a period of 42 years (1976 to 2017) were collected from Agro Climate Research Centre, Tamil Nadu Agricultural University. The global climate driver’s viz., Southern Oscillation Index and Indian Ocean Dipole indices were retrieved from Bureau of Meteorology, Australia. K- means algorithm was employed for centroid identification (which select the rows with unique distinguished features) at 90 per cent of the original data for the period of 42 years by eliminating the redundancy nature of the datawhich were used as training set. The result indicated the supremacy and notable strength of RFR over the other algorithms in terms of performance with 89.2 per cent. The Co-efficient of Determination (R2) for the predicted and observed values was found to be 0.8 for the monthly rainfall from 2015 to 2017.

 

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Published

29-03-2022

How to Cite

[1]
V. . GEETHALAKSHMI, S. . KOKILAVANI, S. . RAMANATHAN, G. DHEEBAKARAN, N. . SATHYAMOORTHY, and N. . MARAGATHAM, “Monthly rainfall hind-cast using machine learning algorithms for Coimbatore, Tamil Nadu”, MAUSAM, vol. 73, no. 1, pp. 19–26, Mar. 2022.

Issue

Section

Research Papers

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